Causal and non causal association

• Variables may be associated due to either ‘cause and effect’ or alternative reasons that are not causal
• While all causal relationships are associational, not all associational relationships are causal
• A principal aim of epidemiology is to assess the cause of disease
• Most epidemiological studies are observational rather than experimental, so various possible explanations for an observed association need to be considered before inferring a cause-effect relationship
• Observed associations may be due to chance (random error), bias (systematic error), or confounding
• An association, also called correlation or covariation, is an empirical and statistical relationship between two variables where changes in one variable are connected to changes in the other
• An association may be positive or negative, proportionate or disproportionate, but in itself does not necessarily imply a causal relationship between the two variables
• A causal association is when it can be proved that a change in the independent variable (exposure) produces a change in the dependent variable (disease)
• In non-causal relationships, the relationship between two variables is statistically significant, but no causal relationship exists
• The Bradford-Hill criteria are widely used in epidemiology to assess whether an observed association is likely to be causal
• The three fundamental types of causes, in order of decreasing strength, are (A) sufficient cause, (B) necessary cause, and (C) risk factor
• A risk factor is an exposure, behavior, or attribute that, if present and active, clearly increases the probability of a particular disease occurring in a group of people compared with an otherwise similar group of people who lack the risk factor